Introduction to Large Language Models

Introduction

• Source: Andrew Ng's DeepLearning.AI courses, Andrej Karpathy's "Neural Networks: Zero to Hero"
• Prerequisites: Python basics, basic linear algebra and probability (matrix operations and conditional probability)
• Course difficulty: 🌟🌟🌟
• Estimated study hours: 20+

Large Language Models (LLMs) are the foundation of today's AI technology. Whether you want to do prompt engineering, RAG, or Agents, understanding the fundamentals of LLMs is a necessary starting point. This course won't dive into paper-level mathematical derivations — instead, it focuses on building intuition: helping you understand why models "can talk," why they "make mistakes," and how to "train them."

Traditional NLP required training separate models for each task (translation, summarization, classification). The revolutionary aspect of LLMs is: one model handles all language tasks, and can even generalize to tasks it never saw during training. Three things power this: the Transformer architecture, large-scale pre-training, and human feedback alignment (RLHF).

Course Resources

• Course website: DeepLearning.AI Short Courses
• Course videos: Andrej Karpathy - Neural Networks: Zero to Hero (YouTube)
• Supplementary reading: Andrej Karpathy - The Unreasonable Effectiveness of Recurrent Neural Networks

Resource Summary

Must-watch for beginners (building intuition):

• 3Blue1Brown Neural Networks — Visual explanations of neural networks, beginner-friendly
• Andrej Karpathy - Neural Networks: Zero to Hero — Implement backpropagation and GPT from scratch, excellent free course
• Andrew Ng - ChatGPT Prompt Engineering for Developers — 2-hour quick start, covers LLM usage and principles overview

Systematic study (deeper understanding):

• Andrew Ng - Generative AI for Everyone — Coursera course, 16 hours, great for building a systematic understanding
• HuggingFace NLP Course — Free, covers Tokenizer, model architecture, fine-tuning, and full pipeline
• Stanford CS229 (Machine Learning) — Traditional ML fundamentals, understanding the "past life" of LLMs

Papers & explainers:

• Attention Is All You Need — The original Transformer paper, worth reading at least the abstract and figures
• Andrej Karpathy - State of GPT (2023) — The complete GPT training pipeline explained in one hour
• Jay Alammar - The Illustrated Transformer — The most intuitive visual explanation of Transformers

Notes

If you're short on time, the most efficient path is: 3Blue1Brown Neural Networks series (2h) → Karpathy Zero to Hero first 3 lessons (4h) → Andrew Ng ChatGPT course (2h). About 8 hours total to build a solid intuition for how LLMs work. Don't start with papers — build intuition first, then go deeper progressively.